Transport planning is a difficult arena to operate in. This is due to the inherently "wicked nature" of the issues at stake coupled with the complexity of urban environments [1]}, [2]}. Advancements in understanding human mobility have recently been made through the use of computational methodologies (see for instance [3]}, [4]}, [5]}, [6]}). However, the need to operationalize and distill such methods into actionable tools tailored to real-world problems is still present. In particular, the humanitarian sector might benefit from the application of models and methods coming from the new science of data. Our proposed Achilles application is a step forward in this direction, providing an easy-to-use interface on top of complex models that allow non-expert users, such as non-profit organizations, to rapidly prototype different stress scenarios. It represents thus a tentative example of how data science can be put to good use to tackle issues of social relevance. In the presented experiments we tested the case of Singapore, particularly suited due to its natural geography and the frequent disruption thereby caused on the bus transport network system by floods. Since the application is built deploying data that are not site-specific (e.g. O-D matrices), it is scalable to different environments, making it suitable to be applied even in fragile contexts. Further, due to its flexible architecture Achilles accommodates new data sources with ease, adapting its modelling capacity to meet local needs. As unpredictable stress events might present themselves in the future, transport planners need to be vary of mutable conditions. This requires not only a close monitoring of transport networks under normal operational settings, but the ability to quickly respond to new challenges [7]}. The solution we advance lies precisely in this direction.
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